High Performance Computing (HPC Lab)
The Sixth International Workshop on
Systems and Network Telemetry and Analytics (SNTA 2023)
Orlando, Florida, USA (June 20, 2023)
(in conjunction with ACM HPDC2023, June 16 - June 23, 2023)
The tasks of systems and network telemetry are a key element for effective operations and management of HPC and distributed computing systems, by offering comprehensive monitoring and analysis capabilities to provide the visibility into what is occurring at any time. The tasks will be significantly complicated with the greater complexity of computing systems, increasing network speed, and the newly introduced mobile and IoT devices. Such changes will render the existing telemetry and analysis techniques outdated, and more scalable techniques may be in place for data-driven and deeper data analysis. In addition to the quantitative and qualitative challenges, data pressure in systems and networks also comes from various sources such as sensors, computing systems, networking and security devices, and other emerging computing elements speaking with different syntax and semantics, which makes organizing and incorporating the generated data difficult for extensive analysis.
This workshop aims at bridging the systems and network measurement and the latest advances in artificial intelligence and data science technologies, to advance the performance and reliability of HPC and distributed systems. New analysis techniques are needed in the modern world, from the diverse angles of systems/network performance, availability, and security. For example, real-time streaming analytics algorithms and methods need to be explored for estimating network performance and summarizing the traffic variables to capture the network activities due to the network bandwidth increase. Multivariate analysis of telemetric variables may be able to provide an intuitive, comprehensive view of the systems and networks dynamics. New logging techniques are also needed in the future with the latest development in the storage and archival technologies. In addition, many applications in this area may need to address the application-specific requirements and challenges. This workshop intends to share visions of investigating new approaches and methods at the intersection of data sciences and HPC/distributed computing systems.
- Systems and network measurement, analysis, and summarization
- Data-driven, multivariate, streaming-based data processing
- Distributed and federated machine learning
- Cybersecurity, forensics, privacy, and anonymization
- Smart instruments, edge/fog-systems, and IoTs
- Wireless, sensor, 5G/6G networks measurement and analysis
- Software/Knowledge-Defined-* technologies
- Advances in network and storage technologies
- Intelligent workflow, visualization, and applications
- Performance modeling, analysis, and engineering
- Design and evaluation of HPC and distributed systems
- Best practices and implementations tied to systems/network data analysis
All papers must be original and not simultaneously submitted to another journal or conference. Authors are invited to submit either a full (max 8 pages) paper or a short/work-in-progress (max 4 pages) paper. Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library.
Submission link: https://easychair.org/conferences/?conf=snta23
- Submission deadline:
March 31, 2023April 9, 2023
- Author notification:
April 14, 2023April 21, 2023
- Camera-ready deadline: May 3, 2023
- Mohammed Abuhamad, Loyola University Chicago, USA
- Stergios Anastasiadis, University of Ioannina, Greece
- Engin Arslan, University of Nevada at Reno, USA
- Sang-Yoon Chang, University of Colorado at Colorado Springs, USA
- Ming-Hung Chen, IBM, USA
- Chunglae Cho, ETRI, Korea
- Wenjun Fan, Xi'an Jiaotong-Liverpool University, China
- In Kee Kim, University of Georgia, USA
- Alina Lazar, Youngstown State University, USA
- Chul-Ho Lee, Texas State University, USA
- Dongeun Lee, Texas A&M University-Commerce, USA
- Che-Rung lee, National Tsing Hua University, Taiwan
- Kwangsung Oh, University of Nebraska Omaha, USA
- Amir H. Payberah, KTH Royal Institute of Technology, Sweden
- Marco Pulimeno, University of Salento, Italy
- Thomas Ropars, Univ. Grenoble Alpes, France
- Alex Sim, Lawrence Berkeley National Laboratory, USA
- Houjun Tang, Lawrence Berkeley National Laboratory, USA
- K. John Wu, Lawrence Berkeley National Laboratory, USA
- Massimo Cafaro, University of Salento, Italy
- Eric Chan-Tin, Loyola University Chicago, USA
- Jerry Chou, National Tsing Hua University, Taiwan
- Jinoh Kim, Texas A&M University-Commerce, USA
Title: Understanding the Privacy Dimension of Wearables through Machine Learning-enabled Inferences
- Dr. David Mohaisen, University of Central Florida, USA
Abstract: To keep up with the ever-growing user expectations, developers keep adding new features to augment the use cases of wearables, such as fitness trackers, augmented reality head-mounted devices (AR HMDs), and smart watches, without considering their security and privacy impacts. In this talk, I will introduce our recent results on understanding the privacy dimension of wearables through inference attacks facilitated by machine learning and open research directions. First, I will present an exploration of the attack surface introduced by fitness trackers. We propose an inference attack that breaches location privacy through the elevation profiles collected by fitness trackers. Our attack highlights that adversaries can infer the location from elevation profiles collected via fitness trackers. Second, I will review the attack surface introduced by smartwatches by developing an inference attack that exploits the smartwatch microphone to capture the acoustic emanations of physical keyboards and successfully infers what the user has been typing. Third, I will present an exploration of the AR HMD’s through the design of an inference attack that exploits the geometric projection of hand movements in the air. The attack framework predicts the typed text on an in-air tapping keyboard, which is only visible to the user. I will conclude with lessons learned, defense directions, and open research directions.
Biography: David Mohaisen (PhD’12, University of Minnesota) is a Full Professor of Computer Science at the University of Central Florida, where he has been since 2017. Previously, he was an Assistant Professor at SUNY Buffalo (2015-2017) and a Senior Scientist at Verisign Labs (2012-2015). His research interests are in applied security and privacy, covering aspects of networked systems, software systems, IoT and AR/VR, machine learning, and blockchain systems. His research has been supported by several generous grants from NSF, NRF, AFRL, AFOSR, etc., and has been published in top conferences and journals, with multiple best paper awards. His work was featured in multiple outlets, including the New Scientist, MIT Technology Review, ACM Tech News, Science Daily, etc. Among other services, he is currently an Associate Editor of IEEE Transactions on Dependable and Secure Computing and served as an Associate Editor of IEEE Transactions on Mobile Computing (2 terms), IEEE Transactions on Parallel and Distributed Systems (1 term), and IEEE Transactions on Cloud Computing (1 term). He is a senior member of ACM (2018) and IEEE (2015), a Distinguished Speaker of the ACM, and a Distinguished Visitor of the IEEE Computer Society. Visit https://www.cs.ucf.edu/~mohaisen/ for more information.
Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library.
The conference will be held in conjunction with ACM HPDC 2023 (June 16 - June 23, 2023).
All questions about submissions should be emailed to SNTA.email@example.com.